
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase = None  ,     )	->						int:
				UpperCamelCase_:				Union[str, Any]						=    {}

				if train_file is not None:
								UpperCamelCase_:				int						=    [train_file]
				if eval_file is not None:
								UpperCamelCase_:				Optional[Any]						=    [eval_file]
				if test_file is not None:
								UpperCamelCase_:				Dict						=    [test_file]

				UpperCamelCase_:				List[str]						=    datasets.load_dataset("""csv"""  ,     data_files=lowerCamelCase    )
				UpperCamelCase_:				Tuple						=    list(ds[list(files.keys()    )[0]].features.keys()    )
				UpperCamelCase_:				Optional[int]						=    features_name.pop(lowerCamelCase    )
				UpperCamelCase_:				Optional[Any]						=    list(set(ds[list(files.keys()    )[0]][label_name]    )    )
				UpperCamelCase_:				int						=    {label: i for i, label in enumerate(lowerCamelCase    )}
				UpperCamelCase_:				Any						=    tokenizer.model_input_names
				UpperCamelCase_:				int						=    {}

				if len(lowerCamelCase    ) == 1:
								for k in files.keys():
												UpperCamelCase_:				List[str]						=    ds[k].map(
												    lambda lowerCamelCase    : tokenizer.batch_encode_plus(
												        example[features_name[0]]  ,     truncation=lowerCamelCase  ,     max_length=lowerCamelCase  ,     padding="""max_length"""    )  ,     batched=lowerCamelCase  ,     )
				elif len(lowerCamelCase    ) == 2:
								for k in files.keys():
												UpperCamelCase_:				str						=    ds[k].map(
												    lambda lowerCamelCase    : tokenizer.batch_encode_plus(
												        (example[features_name[0]], example[features_name[1]])  ,     truncation=lowerCamelCase  ,     max_length=lowerCamelCase  ,     padding="""max_length"""  ,     )  ,     batched=lowerCamelCase  ,     )

				def gen_train():
								for ex in transformed_ds[datasets.Split.TRAIN]:
												UpperCamelCase_:				Optional[int]						=    {k: v for k, v in ex.items() if k in input_names}
												UpperCamelCase_:				Tuple						=    labelaid[ex[label_name]]
												yield (d, label)

				def gen_val():
								for ex in transformed_ds[datasets.Split.VALIDATION]:
												UpperCamelCase_:				Union[str, Any]						=    {k: v for k, v in ex.items() if k in input_names}
												UpperCamelCase_:				int						=    labelaid[ex[label_name]]
												yield (d, label)

				def gen_test():
								for ex in transformed_ds[datasets.Split.TEST]:
												UpperCamelCase_:				Tuple						=    {k: v for k, v in ex.items() if k in input_names}
												UpperCamelCase_:				Optional[int]						=    labelaid[ex[label_name]]
												yield (d, label)

				UpperCamelCase_:				str						=    (
				    tf.data.Dataset.from_generator(
				        lowerCamelCase  ,     ({k: tf.intaa for k in input_names}, tf.intaa)  ,     ({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))  ,     )
				    if datasets.Split.TRAIN in transformed_ds
				    else None
				)

				if train_ds is not None:
								UpperCamelCase_:				Optional[Any]						=    train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]    )    )    )

				UpperCamelCase_:				Union[str, Any]						=    (
				    tf.data.Dataset.from_generator(
				        lowerCamelCase  ,     ({k: tf.intaa for k in input_names}, tf.intaa)  ,     ({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))  ,     )
				    if datasets.Split.VALIDATION in transformed_ds
				    else None
				)

				if val_ds is not None:
								UpperCamelCase_:				int						=    val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]    )    )    )

				UpperCamelCase_:				Dict						=    (
				    tf.data.Dataset.from_generator(
				        lowerCamelCase  ,     ({k: tf.intaa for k in input_names}, tf.intaa)  ,     ({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))  ,     )
				    if datasets.Split.TEST in transformed_ds
				    else None
				)

				if test_ds is not None:
								UpperCamelCase_:				List[str]						=    test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]    )    )    )

				return train_ds, val_ds, test_ds, labelaid


lowerCamelCase_    :			List[str]    =	logging.getLogger(__name__)

@dataclass
class 							_UpperCamelCase					:






			'''simple docstring'''

			__UpperCamelCase   :    int          =			field(metadata={"""help""": """Which column contains the label"""}	)
			__UpperCamelCase   :    str          =			field(default=_A  ,							metadata={"""help""": """The path of the training file"""}	)
			__UpperCamelCase   :    Optional[str]          =			field(default=_A  ,							metadata={"""help""": """The path of the development file"""}	)
			__UpperCamelCase   :    Optional[str]          =			field(default=_A  ,							metadata={"""help""": """The path of the test file"""}	)
			__UpperCamelCase   :    int          =			field(
			    default=128  ,							metadata={
			        """help""": (
			            """The maximum total input sequence length after tokenization. Sequences longer """
			            """than this will be truncated, sequences shorter will be padded."""
			        )
			    }  ,							)
			__UpperCamelCase   :    bool          =			field(
			    default=_A  ,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""}	)




@dataclass
class 							_UpperCamelCase					:






			'''simple docstring'''

			__UpperCamelCase   :    str          =			field(
			    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}	)
			__UpperCamelCase   :    Optional[str]          =			field(
			    default=_A  ,							metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}	)
			__UpperCamelCase   :    Optional[str]          =			field(
			    default=_A  ,							metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}	)
			__UpperCamelCase   :    bool          =			field(default=_A  ,							metadata={"""help""": """Set this flag to use fast tokenization."""}	)
			# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
			# or just modify its tokenizer_config.json.
			__UpperCamelCase   :    Optional[str]          =			field(
			    default=_A  ,							metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}  ,							)







def 							A__       (    )	->						Optional[int]:
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.
				UpperCamelCase_:				Optional[Any]						=    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
				UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				List[Any]						=    parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir    )
				    and os.listdir(training_args.output_dir    )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
								    """ --overwrite_output_dir to overcome."""    )

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""  ,     datefmt="""%m/%d/%Y %H:%M:%S"""  ,     level=logging.INFO  ,     )
				logger.info(
				    F'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1    )}, '''
				    F'''16-bits training: {training_args.fpaa}'''    )
				logger.info(F'''Training/evaluation parameters {training_args}'''    )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				UpperCamelCase_:				int						=    AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,     cache_dir=model_args.cache_dir  ,     )

				UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				int						=    get_tfds(
				    train_file=data_args.train_file  ,     eval_file=data_args.dev_file  ,     test_file=data_args.test_file  ,     tokenizer=lowerCamelCase  ,     label_column_id=data_args.label_column_id  ,     max_seq_length=data_args.max_seq_length  ,     )

				UpperCamelCase_:				List[str]						=    AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,     num_labels=len(lowerCamelCase    )  ,     labelaid=lowerCamelCase  ,     idalabel={id: label for label, id in labelaid.items()}  ,     finetuning_task="""text-classification"""  ,     cache_dir=model_args.cache_dir  ,     )

				with training_args.strategy.scope():
								UpperCamelCase_:				Dict						=    TFAutoModelForSequenceClassification.from_pretrained(
								    model_args.model_name_or_path  ,     from_pt=bool(""".bin""" in model_args.model_name_or_path    )  ,     config=lowerCamelCase  ,     cache_dir=model_args.cache_dir  ,     )

				def compute_metrics(lowerCamelCase    ) -> Dict:
								UpperCamelCase_:				Optional[Any]						=    np.argmax(p.predictions  ,     axis=1    )

								return {"acc": (preds == p.label_ids).mean()}

				# Initialize our Trainer
				UpperCamelCase_:				Any						=    TFTrainer(
				    model=lowerCamelCase  ,     args=lowerCamelCase  ,     train_dataset=lowerCamelCase  ,     eval_dataset=lowerCamelCase  ,     compute_metrics=lowerCamelCase  ,     )

				# Training
				if training_args.do_train:
								trainer.train()
								trainer.save_model()
								tokenizer.save_pretrained(training_args.output_dir    )

				# Evaluation
				UpperCamelCase_:				Union[str, Any]						=    {}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***"""    )
								UpperCamelCase_:				str						=    trainer.evaluate()
								UpperCamelCase_:				Optional[int]						=    os.path.join(training_args.output_dir  ,     """eval_results.txt"""    )

								with open(lowerCamelCase  ,     """w"""    ) as writer:
												logger.info("""***** Eval results *****"""    )

												for key, value in result.items():
																logger.info(F'''  {key} = {value}'''    )
																writer.write(F'''{key} = {value}\n'''    )

												results.update(lowerCamelCase    )

				return results


if __name__ == "__main__":
							main()








import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Tuple          =			(CMStochasticIterativeScheduler,)
			__UpperCamelCase   :    List[str]          =			10

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						**snake_case_			:							List[str]   ):
							UpperCamelCase_:				Optional[int]						=    {
							    """num_train_timesteps""": 201,
							    """sigma_min""": 0.002,
							    """sigma_max""": 80.0,
							}

							config.update(**snake_case_   )
							return config

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							UpperCamelCase_:				Tuple						=    10

							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    self.scheduler_classes[0](**snake_case_   )

							scheduler.set_timesteps(snake_case_   )

							UpperCamelCase_:				int						=    scheduler.timesteps[0]
							UpperCamelCase_:				str						=    scheduler.timesteps[1]

							UpperCamelCase_:				Optional[int]						=    self.dummy_sample
							UpperCamelCase_:				int						=    0.1 * sample

							UpperCamelCase_:				Optional[Any]						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample
							UpperCamelCase_:				Any						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample

							self.assertEqual(output_a.shape    ,						sample.shape   )
							self.assertEqual(output_a.shape    ,						output_a.shape   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for clip_denoised in [True, False]:
											self.check_over_configs(clip_denoised=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Any   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				List[str]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Optional[int]						=    1
							scheduler.set_timesteps(snake_case_   )
							UpperCamelCase_:				Any						=    scheduler.timesteps

							UpperCamelCase_:				int						=    torch.manual_seed(0   )

							UpperCamelCase_:				Any						=    self.dummy_model()
							UpperCamelCase_:				Union[str, Any]						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(snake_case_   ):
											# 1. scale model input
											UpperCamelCase_:				str						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				List[Any]						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				Union[str, Any]						=    pred_prev_sample

							UpperCamelCase_:				int						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				List[Any]						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 192.7614   ) < 1e-2
							assert abs(result_mean.item() - 0.2510   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Dict						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[Any]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				str						=    [106, 0]
							scheduler.set_timesteps(timesteps=snake_case_   )
							UpperCamelCase_:				int						=    scheduler.timesteps

							UpperCamelCase_:				List[Any]						=    torch.manual_seed(0   )

							UpperCamelCase_:				List[Any]						=    self.dummy_model()
							UpperCamelCase_:				int						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for t in timesteps:
											# 1. scale model input
											UpperCamelCase_:				Optional[int]						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				Dict						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				List[str]						=    pred_prev_sample

							UpperCamelCase_:				Union[str, Any]						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				str						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 347.6357   ) < 1e-2
							assert abs(result_mean.item() - 0.4527   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				int						=    self.scheduler_classes[0]
							UpperCamelCase_:				Optional[int]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Any						=    [39, 30, 12, 15, 0]

							with self.assertRaises(snake_case_    ,						msg="""`timesteps` must be in descending order."""   ):
											scheduler.set_timesteps(timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Union[str, Any]						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Tuple						=    [39, 30, 12, 1, 0]
							UpperCamelCase_:				List[Any]						=    len(snake_case_   )

							with self.assertRaises(snake_case_    ,						msg="""Can only pass one of `num_inference_steps` or `timesteps`."""   ):
											scheduler.set_timesteps(num_inference_steps=snake_case_    ,						timesteps=snake_case_   )




			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				int						=    self.get_scheduler_config()
							UpperCamelCase_:				int						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				int						=    [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    snake_case_    ,						msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""    ,						):
											scheduler.set_timesteps(timesteps=snake_case_   )






